No.122 Analysing Large Collections of Time Series

Time: Monday, 11.00 am

Speaker: Dr. Ben D Fulcher, Monash University, Australia

Title: Feature-based time-series analysis  (Slides)

Abstract: I will give an introduction to feature-based approaches to time-series analysis. I will summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis will be given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. I argue that the future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.

 

Time: Monday, 1.30 pm

Speaker: Dr. Julie Novak, Netflix, USA

Title: Challenges in Forecasting High-Dimensional Time Series (Slides)

Abstract: This talk provides an overview of the challenges faced when forecasting high-dimensional time series data and the methods used to address them. We motivate the topic by describing issues that arise when forecasting IBM’s quarterly revenue for all their divisions and markets. It is often the case that such high-dimensional time series are naturally structured in a hierarchical manner. As a result, the forecast reconciliation problem becomes a critical one, where the goal is to make sure that forecasts produced independently at each node of the hierarchy are aggregate consistent while remaining as accurate as possible. We review the state of the art methodology that practitioners currently use and highlight recent advances in the field. Finally we discuss open questions and future research directions in this area.

 

Time: Monday, 2.15 pm

Speaker:  Prof. Dan Feldman, University of Haifa, Israel

Title: Core-sets for learning streaming signals in real-time  (Slides)

Abstract: A coreset (or, core-set) for a given problem is a “compressed” representation of its input, in the sense that a solution for the problem with the (small) coreset as input would yield a provable (1+epsilon) factor approximation to the problem with the original (large) input.

Using traditional techniques, a coreset usually implies provable linear time algorithms for the corresponding optimization problem, which can be computed in parallel on the cloud/GPU, via one pass over Big data, and using only logarithmic space (i.e, in the streaming model).

In this talk I will survey main coresets techniques, with applications for real-time signal processing such as localization of nano-drones, GPS data, and new coresets for deep learning.

 

Time: Tuesday, 9.00 am

Speaker: Prof. Alexander Aue, University of California, Davis, USA

Title: Functional data analysis, with a view on current time series methods   (Slides)

Abstract: In this talk, I will trace the broader developments within the field of functional data analysis that have taken place during the past two or so decades, with attention focused on the case of dependent functional observations. I will discuss by way of examples the most important tools of statistical inference, such as dimension reduction techniques, for independent data, explain what issues arise under dependence and how these may be resolved. These general considerations will then be utilized to give an overview of more specialized prediction algorithms and estimation strategies for functional time series. The talk will conclude with some speculation about future research directions.

 

Time: Tuesday, 9.45 am

Speaker: Prof. Bei Wang, University of Utah, USA

Title: Topological Data Analysis In a Nutshell

Abstract: Topological Data Analysis (TDA) is an emerging area in exploratory data analysis and data  visualization that has had a growing interests and notable successes with an expanding research community.
Topological techniques which capture the “shape of data” have the potential to extract salient features and to provide robust descriptions of large and complex (i.e., high throughput, high-dimensional, incomplete and noisy) data. In this talk, I will survey some of the classic topological techniques, with a focus on their applications in data analysis and data visualization. I will also briefly touch on the new opportunities
connecting TDA with time series analysis.

 

Time: Wednesday, 9.00 am

Speaker:  Prof. Kevin Buchin, TU Eindhoven, the Netherlands and  Prof. Maike Buchin, Ruhr-Universität Bochum, Germany

Title: Trajectory Segmentation and Clustering

Abstract: Nowadays more and more movement data is being collected, of people, animals, and vehicles. Analysing such data requires efficient algorithms. We first give a brief overview of work in this field, and then focus on algorithms for two analysis tasks: segmentation and clustering. Segmentation asks to split and possibly group trajectories such that they have similar movement characteristics. We present geometric and model-based approaches to segmentation, and show how these can be used to classify subtrajectories based on their characteristics. Clustering asks to group similar trajectories or subtrajectories. We present algorithmic results for clustering based on geometric similarity measures